Hướng nghiên cứu BM CSKTĐ

Transcription

1 Hướng nghiên cứu BM CSKTĐ My-Ha Le Ho Chi Minh City University of Technology and Education June 23, 2015

2 Mục tiêu Đáp ứng nhu cầu thực tế trong công nghiệp Giảm chi phí/giá thành Chuyển giao công nghệ Tham gia các đề tài nghiên cứu khoa học Viết bài đăng trên transaction/journal/conference Tham gia các hội nghị quốc tế Hỗ trợ GV trẻ cách nghiên cứu, xin HB 2

3 Research Orientations Computer Vision/ image processing Robotics and automation Dynamic stability of power systems Green energy Computer Vision Robotics ISLab. Green energy Power Systems 3

4 Motivation (1) How to model the scene 4

5 Motivation (2) V.O V.O+GPS How to recovery camera moti on 5

6 Computer vision introduction Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images 6

7 Multiple views geometry I. Convex optimization approach for multiple views geo metry 1. 3D scene modeling 2. Omnidirectional camera motion recovery II. Localization of Vehicle-Mounted Vision Sensor 1. Omnidirectional camera in long-range motion 2. CCD camera for real-time application 7

8 Multiple views geometry R i : Rotation matrix T i : Translation vector U i : 3D points U 1 U 4 U 3 U 2 U 5 U 6 U 7 minimize f(r,t,u) Camera 1 R 1,T 1 8 Camera 2 R 2,T 2 Camera 3 R 3,T 3

9 Proposed scheme for 3D scene modeling Context analysis and object filtering Feature detection Feature matching Classified scene Filtered image Initial recovery Pair-wise rotation estimation 5-points RANSAC Global rotation Motion averaging 3Dpoints and camera recovery Quasi-convex optimization Global bundle adjustment Stereo mapping 9

10 Large scale scene model Data: 322 images Size 2048x

11 Large scale scene model Data: 322 images Size 2048x1536 My-Ha Le, Andrey Vavilin and Kang-Hyun Jo, "3D Scene Reconstruction Enhancement Method based on Automatic Context Analysis and Convex Optimization", Neurocomputing, Volume 137, Pages 71-78, ISSN ,

12 Proposed scheme for camera motion recovery Omnidirectional images Vanishing point extraction Pair-wise rotation estimation Global rotation estimation Convex optimization Camera motion recovery 12

13 Camera motion recovery The line extraction is robust and high accuracy Rotation angle Camera motion recovery 13

14 Localization of Vehicle-Mounted Vision Sensor Frames extraction Edge matching Correspondence points Translation calculation Yaw angle estimation Pitch angle estimation GPS receiver Position estimation GPS data evaluation High accuracy GPS F T Position correction Vehicle localization 14

15 Omnidirectional camera in long-range motion Hardware Rotation angle (ya w) Beta angle (radian) Frames 15

16 Omnidirectional camera in long-range motion Method VO VO + GPS Max. err or Error analysis [m] Localization result Method 2D assumptio n 3D trajectory Distanc e Error 10.85% 3.89% My-Ha Le, Van-Dung Hoang, Andrey Vavilin and Kang-Hyun Jo, One-Point-plus for 5-DoF Localization of Vehicle-Mounted Omnidirectional Camera in Long-Range Motion ", IJCAS, Vol.11, No.5, pp ,

17 CCD camera in long range motion Hardware systems 17

18 CCD camera in long range motion Simultaneous 3D reconstruction and localization 621 frames are processed Lê Mỹ Hà, Ngô Văn Thuyên, xây dư ng môi trươ ng la m viê c 3D va tư đi nh vi cho robot ", Đề tài cấp trường trọng điểm, SPKT.TPHCM,

19 Conclusions (1) Convex optimization approach for multiple view geo metry Advantages Removing outliers by context analysis in pre-process Comparing with increment BA, This method overcome poor results cause by bad initial points cho sen, Overcoming drift and local minima problem Generating robust structure with small baseline images Disadvantage This method is sensitive to outliers Time consuming 19

20 Conclusions (2) Application to long-range navigation Advantages Improved the 2D motion assumption Solved cumulative errors in long-range odometry The visual odometry obtained for GPS position correctio n Compensate each other when losing one of two guidanc e Disadvantage Sensitive to fast moving objects Require high frame rate 20

21 Future works 3D object measurement and tracking for collision avo idance Develop the Driver Assistance Systems (DAS) Autonomous/intelligent vehicle 21

22 Future works Install the controller system Scene understanding 3D modeling Object recognition Autonomous localization Performing individual task 22

23 Multiple vehicle detection and tracking Main Goal: Multiple vehicle detection and tracking Challenge work: -Different environment(illumination, distance, background ) -Tracking windows scale dynamic adjustment -Vehicle partial occlusion -Vehicle temporary missing 23

24 System architecture Frame 1 Frame 2 Frame n Vehicle detection unit Vehicle detection unit Vehicle detection unit Detected new vehicle? Detected new vehicle? Detected new vehicle? yes yes yes Particle filter 1 Particle filter 2 Particle filter m Vehicle Tracking Unit Internal storage Return to Vehicle detection unit 24

25 Experiments Result Frame 2 Frame 80 Frame 180 Frame 260 Frame 280 Frame

26 Hoạch định đường đi Tôi phải đi đâu? (Tìm đường đi) Tìm đường đi là vấn đề quan trọng nhất trong lĩnh vực robot tự hành. 4:53:56 PM 26

27 Hoạch định đường đi giữa 2 điểm Đường đi giữa 2 điểm A và B Quỹ đạo đường đi phải bằng phẳng. Đường đi phải ngắn nhất. Đường đi phải an toàn. 4:53:56 PM 27 Đường đi giữa 2 điểm A và B

28 Kết quả mô phỏng 28

29 Kết quả mô phỏng Đường đi theo phương pháp D*_PF Đường đi theo phương pháp PSO 4:53:56 PM 29

30 Kết quả mô phỏng Đường đi theo phương pháp D*_PF Đường đi theo phương pháp PSO 4:53:56 PM 30

31 Human detection Training stage Examples Features description Training Model Detection stage Input image Candidate regions Features description Classification Human regions Postprocess Human regions 31

32 Experimental results 32

33 Human face detection Application use for face detection and recognition that enables tracking or identification of users when intelligent robot captures video sequences. 33

34 Method 34

35 License plate recognition algorithm 35

36 Problems 36

37 Characters separation 2 37

38 Character recognition 38

39 Character recognition 39

40 Decreasing capacity of jpeg2000 standard image on FPGA board Avalon source Avalon sink Avalon source Avalon sink Avalon source Avalon sink Avalon sink Avalon source Avalon source Avalon sink Avalon source Avalon sink Avalon source Avalon sink Avalon sink Avalon source Avalon sink Avalon source Avalon sink Avalon source Avalon sink Avalon source Avalon source Avalon sink Avalon sink Avalon source Avalon source Avalon sink External Memory Avalon MM Source IF Tier-2 Context Informerg Info MQ Coder Context Coding Rate Control Maxshift JPEG2000 Encoder System ROI Buffer ROI Sink IF DC Level MCT Tiling FDWT Quantization Quantization Buffer Avalon MM Avalon MM External Memory External Memory 40

41 Experiments Image orignal: 128x128 after using Shift up_background method Image orignal: 256x256 after using Shift up_background method Image orignal: 512x512 after using Shift up_background method Image orignal: 1024x1024 after using Shift up_background method 41

42 Cảm ơn! Contact information: PTN Hệ Thống Thông Minh (Intelligent Systems Laboratory) Khoa Điện-Điện Tử, Đại Học Sư Phạm Kỹ Thuật TPHCM Website: Phone: